CP2.ipynb

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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.2"
    },
    "colab": {
      "name": "CP.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mswastik/optimization/blob/master/CP2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EcrQOnfJgYxd"
      },
      "source": [
        "**Indices**  \n",
        "j - job  \n",
        "m - machine \n",
        "\n",
        "**Decision Variable**  \n",
        "$s_{j,m}$ - start time of job j at machine m  \n",
        "$e_{j,m}$ - end time of job j at machine m  \n",
        "$d_{j,m}$ - duration of job j at machine m  \n",
        "$b_{j,m}$ - machine m is selected for job j     \n",
        "$a_{j,i,m}$ - job i precedes job j at machine m \n",
        "\n",
        "**Parameters**  \n",
        "H - Horizon  \n",
        "$d_j$ - delivery date of job j  \n",
        "$q_j$ - order qty of job j  \n",
        "$o_{j,m}$ - output per hour of job j at machine m  \n",
        "$set_{i,j}$ - setup time between job i & j   \n",
        "\n",
        "\n",
        "**Objective** (Not implemented right now)  \n",
        "$ \\sum_{j} d_j - max(e_{j,m})  \\qquad \\forall m \\in mc$    \n",
        "\n",
        "\n",
        "**Constraints**  \n",
        "$ \\sum_m e_{j,m} < s_{i,m} or s_{j,m} > e_{i,m} \\qquad \\forall j,i \\in jobs$  \n",
        "$ \\sum_j o_{j,m} \\times d_{j,m} >= q_j \\qquad \\forall m \\in ma $    \n",
        "$ \\sum_m s_{i,m} >= e_{j,m} + set_{i,j} \\qquad if \\quad b_{j,m}=1, b_{i,m}=1, a_{j,i,m}=1 $  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAegf4MTD07x",
        "outputId": "cde6225e-43ad-49bd-9691-95b2b46d83d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install ortools\n",
        "#!pip install dtale\n",
        "!pip install -U plotly"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: ortools in /usr/local/lib/python3.6/dist-packages (8.0.8283)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from ortools) (1.15.0)\n",
            "Requirement already satisfied: protobuf>=3.13.0 in /usr/local/lib/python3.6/dist-packages (from ortools) (3.13.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.13.0->ortools) (50.3.2)\n",
            "Requirement already up-to-date: plotly in /usr/local/lib/python3.6/dist-packages (4.12.0)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from plotly) (1.15.0)\n",
            "Requirement already satisfied, skipping upgrade: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly) (1.3.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sG_LW15bCHKH",
        "outputId": "c4df20c1-e08c-4800-b6ba-3d3879284c07",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import collections\n",
        "from ortools.sat.python import cp_model\n",
        "# Clone the entire repo.\n",
        "!git clone -l -s https://github.com/mswastik/optimization.git cloned-repo\n",
        "%cd cloned-repo"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'cloned-repo' already exists and is not an empty directory.\n",
            "/content/cloned-repo\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUhA6-OCCHKV"
      },
      "source": [
        "#Importing Data\n",
        "df=pd.read_excel('orders1.xlsx')\n",
        "ma=pd.read_excel('item master.xlsx')\n",
        "bu=pd.read_excel('item master.xlsx',sheet_name='Sheet2')\n",
        "pa=pd.read_excel('item master.xlsx',sheet_name='packing')\n",
        " \n",
        "# Formating Data\n",
        "df = df[df['Line']=='Tmmthpkhti Limi']\n",
        "df = df[df['Plant']==1024]\n",
        "del df['Line']\n",
        "df['due'] = df['Dispatch Date'] - df['SO Date'].min()\n",
        "df['due'] = df['due'].dt.days\n",
        "df['sodate'] = df['SO Date'] - df['SO Date'].min()\n",
        "df['sodate'] = df['sodate'].dt.days\n",
        "df.reset_index(inplace=True,drop=True)\n",
        "df['Key']=df['SO'].astype('str')+df['FG Code']\n",
        "df.drop(['Dispatch Date','SO Date','Description','Customer'],axis=1,inplace=True)\n",
        "df['Order Qty'] = df[df['Order Qty']!=0]['Order Qty']\n",
        "df['Order Qty'] = df['Order Qty']*1000\n",
        "df=df[df['Key']!='17023287gd39000800iX']\n",
        " \n",
        " \n",
        "# Creating Parameter Data\n",
        "jobs =df['Key']\n",
        "op =[1,2]  # Ignore as of now\n",
        "mc = [1,2,3,4,5,6]\n",
        "#horizon = max(df[\"due\"])+900\n",
        "due = df.set_index('Key')\n",
        "dur=df[['Key','FG Code','Order Qty']]\n",
        "dur=dur.merge(pa[[\"FG Code\",\"Line\",\"Output\"]], on=\"FG Code\")\n",
        "dur['Output']=dur['Output']*100\n",
        "dur['duration']=dur['Order Qty']/dur['Output']\n",
        "dur['duration'] = dur['duration'].astype('float64').round(decimals = 2)\n",
        "dur['operation'] = 2\n",
        "budf=df.merge(ma[[\"FG Code\",\"Bulk Code\",\"Case Size\",\"Pack Wt(g)\"]],on='FG Code',how='left')\n",
        "budf['Qty']=budf['Order Qty']*budf['Case Size']*budf['Pack Wt(g)']/1000000\n",
        "\n",
        "pasp= pa.groupby('FG Code').mean()\n",
        "budf=budf.merge(pasp,on='FG Code')\n",
        "budf['cons'] = budf['Speed']*budf['Pack Wt(g)']*60/1000\n",
        "budf.drop(['Speed','Line'],axis=1,inplace=True)\n",
        "\n",
        "bdur=budf.merge(bu[[\"Bulk Code\", \"Line\",\"Output\"]],on=\"Bulk Code\",how=\"left\")\n",
        "bdur['Output'] = bdur['Output'].replace(0,1)\n",
        "bdur['duration'] = bdur['Qty']/bdur['Output']\n",
        "bdur['duration'] = bdur['duration'].fillna(0).astype('int')\n",
        "bdur['operation'] = 1\n",
        "bdur['Order Qty'] = bdur['Qty']\n",
        "bdur.drop(['Plant','due','sodate','Case Size','Pack Wt(g)','SO','Qty','Bulk Code'],axis=1,inplace=True)\n",
        "dur=dur.append(bdur)\n",
        "dur.set_index(['Key','Line','operation'],inplace=True)\n",
        "del dur['FG Code']\n",
        "dur['Output'].replace(0,1,inplace=True)\n",
        "dur['Order Qty']=dur['Order Qty'].fillna(0).astype('int')\n",
        "dur['duration']=dur['duration'].fillna(0).astype('int')\n",
        "dur['Output']=dur['Output'].fillna(0).astype('int')\n",
        "horizon = sum(dur['duration'])\n",
        " \n",
        "#Setup\n",
        "setup = pd.DataFrame(columns=('Key1','Key2','setup'))\n",
        "for i in df['Key']:\n",
        "  for k in df['Key']:\n",
        "    if df[df['Key']==i][\"FG Code\"].values[0]==df[df['Key']==k][\"FG Code\"].values[0]:\n",
        "      f = {'Key1':[i],\"Key2\":[k],\"setup\":[0]}\n",
        "    else:\n",
        "      f = {'Key1':[i],\"Key2\":[k],\"setup\":[4]}\n",
        "    f = pd.DataFrame(data=f)\n",
        "    setup = pd.concat([setup,f])\n",
        "setup.set_index(['Key1','Key2'],inplace=True)\n",
        "pasp= pa.groupby('FG Code').mean()\n",
        "budf=budf.merge(pasp,on='FG Code')\n",
        "budf['cons'] = budf['Speed']*budf['Pack Wt(g)']*60/1000"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PLXXz4aK1F3d"
      },
      "source": [
        ""
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uexm8ntU7NZf",
        "outputId": "58282055-3575-403c-d396-962816f8c62c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "dur"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Order Qty</th>\n",
              "      <th>Output</th>\n",
              "      <th>duration</th>\n",
              "      <th>cons</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Key</th>\n",
              "      <th>Line</th>\n",
              "      <th>operation</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">17023287gd29904300iX</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <td>486000</td>\n",
              "      <td>2250</td>\n",
              "      <td>216</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <th>2</th>\n",
              "      <td>486000</td>\n",
              "      <td>2000</td>\n",
              "      <td>243</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <th>2</th>\n",
              "      <td>486000</td>\n",
              "      <td>2250</td>\n",
              "      <td>216</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <th>2</th>\n",
              "      <td>486000</td>\n",
              "      <td>3000</td>\n",
              "      <td>162</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <th>2</th>\n",
              "      <td>486000</td>\n",
              "      <td>2000</td>\n",
              "      <td>243</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">4501763335gC25020000UC</th>\n",
              "      <th>2</th>\n",
              "      <th>1</th>\n",
              "      <td>1440</td>\n",
              "      <td>325</td>\n",
              "      <td>4</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <th>1</th>\n",
              "      <td>1440</td>\n",
              "      <td>325</td>\n",
              "      <td>4</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <th>1</th>\n",
              "      <td>1440</td>\n",
              "      <td>500</td>\n",
              "      <td>2</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <th>1</th>\n",
              "      <td>1440</td>\n",
              "      <td>1</td>\n",
              "      <td>1440</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <th>1</th>\n",
              "      <td>1440</td>\n",
              "      <td>1</td>\n",
              "      <td>1440</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>204 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       Order Qty  Output  duration  cons\n",
              "Key                    Line operation                                   \n",
              "17023287gd29904300iX   1    2             486000    2250       216   NaN\n",
              "                       2    2             486000    2000       243   NaN\n",
              "                       3    2             486000    2250       216   NaN\n",
              "                       4    2             486000    3000       162   NaN\n",
              "                       5    2             486000    2000       243   NaN\n",
              "...                                          ...     ...       ...   ...\n",
              "4501763335gC25020000UC 2    1               1440     325         4  70.0\n",
              "                       3    1               1440     325         4  70.0\n",
              "                       4    1               1440     500         2  70.0\n",
              "                       5    1               1440       1      1440  70.0\n",
              "                       6    1               1440       1      1440  70.0\n",
              "\n",
              "[204 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kluFz3V77yw",
        "outputId": "8549a75e-40b4-4476-c999-864a9bd9db12",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Initialise model\n",
        "model = cp_model.CpModel()\n",
        "\n",
        "# Creating variables to store data\n",
        "task_type = collections.namedtuple('task_type', 'start end dur run1')\n",
        "mtj = collections.defaultdict(list)\n",
        "all_tasks = {}\n",
        " \n",
        "for j in jobs:\n",
        "  for o in op:\n",
        "    for m in mc:\n",
        "      for i in jobs:\n",
        "        if i!=j:\n",
        "          suffix = '_%s_%s_%i_%i' % (j,i,m,o)\n",
        "          start = model.NewIntVar(0, horizon, \"start\"+suffix)\n",
        "          end = model.NewIntVar(0, horizon, \"end\"+suffix)\n",
        "          run1 = model.NewIntVar(0, dur['duration'].loc[(j,m,o)].item(), \"run\"+suffix)\n",
        "          duration = model.NewIntervalVar(start, run1, end, \"duration\"+suffix)\n",
        "          all_tasks[j,i, m,o] = task_type(start=start, end=end, dur= duration, run1=run1)\n",
        "          mtj[o,m].append(duration)\n",
        "\n",
        "# Selection variable to store whether machine is selected to process job\n",
        "b={}\n",
        "for o in op:\n",
        "  for j in jobs:\n",
        "    for m in mc:\n",
        "      for i in jobs:\n",
        "        if i!=j:\n",
        "          b[j,i,m,o] = model.NewBoolVar('selection')\n",
        "          model.Add(all_tasks[j,i, m,o].run1>0).OnlyEnforceIf(b[j,i,m,o])\n",
        "          model.Add(all_tasks[j,i, m,o].run1==0).OnlyEnforceIf(b[j,i,m,o].Not())\n",
        "\n",
        "\"\"\"\n",
        "# Variable to store sequence of jobs\n",
        "a={}\n",
        "for o in op:\n",
        "  for j in jobs:\n",
        "    for m in mc:\n",
        "      for i in jobs:\n",
        "        if j != i:\n",
        "          a[j,i,m,o] = model.NewBoolVar('sequence')\n",
        "          model.Add(all_tasks[i, m,o].start>all_tasks[j, m,o].start).OnlyEnforceIf(a[j,i,m,o])\n",
        "          model.Add(all_tasks[i, m,o].start<all_tasks[j, m,o].start).OnlyEnforceIf(a[j,i,m,o].Not())\n",
        "\"\"\"\n",
        "\n",
        "# Constraint: A machine can process only 1 job at a time \n",
        "for o in op:\n",
        "  for m in mc:\n",
        "    model.AddNoOverlap(mtj[o,m])\n",
        "\n",
        "# Constraint: Complete production of full order quantity\n",
        "com = {}\n",
        "for o in op:\n",
        "  for j in jobs:\n",
        "    for i in jobs:\n",
        "      if i!=j:\n",
        "        tt= 0\n",
        "        for m in mc:\n",
        "          comp = all_tasks[j,i,m,o].run1*dur[\"Output\"].loc[(j,m,o)]\n",
        "          tt = tt + comp\n",
        "        com[j] = tt\n",
        "\n",
        "    #Change ORDER QTY FOR BULK\n",
        "    model.Add(com[j]*10 >= dur.loc[(j,slice(None),o)]['Order Qty'].values[0])\n",
        "  \n",
        "# Constraint of Setup Time\n",
        "for o in op:     \n",
        "  for j in jobs:\n",
        "    for m in mc:\n",
        "      for i in jobs:\n",
        "        if i!=j:\n",
        "          for k in jobs:\n",
        "            if k!=j and k!=i:\n",
        "              model.Add(all_tasks[i,k, m,o].start >= all_tasks[j,i, m,o].end + setup.loc[(j,i)].values[0] ).OnlyEnforceIf(b[j,i,m,o]).OnlyEnforceIf(b[i,k,m,o])\n",
        "          #model.Add(all_tasks[j, m].start >= all_tasks[i, m].end + setup.loc[(j,i)].values[0]).OnlyEnforceIf(a[,i,m].Not()).OnlyEnforceIf(b[j,m]).OnlyEnforceIf(b[i,m])\n",
        "\n",
        "# Delay between jobs due to storage tank full\n",
        "for j in jobs:\n",
        "  for m in mc:\n",
        "    for i in jobs:\n",
        "      if dur.loc[(j,m,1)]['Order Qty']>=3000 and j != i:\n",
        "        for k in jobs:\n",
        "          if k!=i and k!=j: \n",
        "            model.Add(all_tasks[i,k, m,1].start >= all_tasks[j,i, m,1].end+int((dur.loc[(j,m,1)]['Order Qty']-3000)/dur.loc[(j,m,1)]['cons'])).OnlyEnforceIf(b[j,i,m,o]).OnlyEnforceIf(b[i,k,m,o])\n",
        "\n",
        "for j in jobs:\n",
        "  for m in mc:\n",
        "    for j in jobs:\n",
        "      if j!=i:\n",
        "        minst=model.NewIntVar(0, horizon, \"minst\"+j)\n",
        "        model.AddMinEquality(minst,[all_tasks[j,i,k,1].start for k in mc])\n",
        "        model.Add(all_tasks[j,i, m,2].start >= minst+4).OnlyEnforceIf(b[j,i,m,2])\n",
        "\n",
        "gg={}\n",
        "for j in jobs:\n",
        "  for i in jobs:\n",
        "    if i!=j:\n",
        "      aa=model.NewIntVar(0, horizon, \"endrun\"+j)\n",
        "      #aa[m]=all_tasks[j,m].end\n",
        "      model.AddMaxEquality(aa,[all_tasks[j,i,m,2].end for m in mc])\n",
        "      gg[j] = due['due'].loc[j]- aa\n",
        "\n",
        "#Dummy variable for minimizing absolute value in objective function\n",
        "mm={}\n",
        "for j in jobs:\n",
        "  kk = model.NewIntVar(0, horizon, 'dummy'+j)\n",
        "  mm[j] = kk\n",
        "  model.Add(gg[j]<=mm[j])\n",
        "  model.Add(-gg[j]<=mm[j])\n",
        " \n",
        "model.Minimize(sum([mm[j] for j in jobs]))\n",
        "solver = cp_model.CpSolver()\n",
        "solver.parameters.num_search_workers = 16\n",
        "solver.Solve(model)\n",
        "print(solver.StatusName(),solver.ObjectiveValue())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FEASIBLE 133.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "niO4NG7l8_7d",
        "outputId": "1c35c258-a794-4194-931a-961ca24f604c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "ff=pd.DataFrame()\n",
        "for o in op:\n",
        "  for m in mc:\n",
        "    for j in jobs:\n",
        "      for i in jobs:\n",
        "        if i!=j:\n",
        "          if solver.Value(all_tasks[j,i,m,o].run1)>0:\n",
        "            kk={'Job':j,'line':m,'operation':o,'start':solver.Value(all_tasks[j,i,m,o].start),\n",
        "                'run':solver.Value(all_tasks[j,i,m,o].run1),'end':solver.Value(all_tasks[j,i,m,o].end),'due':due['due'].loc[j],'delay':solver.Value(gg[j])}\n",
        "            ff=ff.append(kk,ignore_index=True)\n",
        "\n",
        "#!pip install -U plotly\n",
        "import plotly.express as px\n",
        "import datetime\n",
        "fig=px.timeline(data_frame=ff, x_start=ff['start'].astype('datetime64[h]'),x_end=ff['end'].astype('datetime64[h]'),\n",
        "                facet_row='line',facet_col='operation',y='Job',height=1200,width=1500,color='Job')\n",
        "fig.update_xaxes(dtick=14400000,tickformat=\"%d-%H\")\n",
        "fig.update_yaxes(autorange=\"reversed\")\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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\"17023572gd43208500iX\", \"17023287gd55102400iX\", \"17023287gd29907800iX\", \"17023287gd29904300iX\"], \"categoryorder\": \"array\", \"domain\": [0.6866666666666666, 0.8283333333333334], \"matches\": \"y\", \"title\": {\"text\": \"Job\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
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              "var gd = document.getElementById('224d3c85-8f56-4102-9c64-d375f04a43fe');\n",
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              "</body>\n",
              "</html>"
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          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iTrYTZe2u6FW",
        "outputId": "fa768e88-6d9e-41ef-8ab2-98af6e322c51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Exploring Result (click on last link to view result)\n",
        "!pip install dtale\n",
        "from dtale import show\n",
        "import dtale.app as dtale_app\n",
        "dtale_app.USE_COLAB = True\n",
        "show(dur,ignore_duplicate=True)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting dtale\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7f/29/935672771acf5a161c60b9eef9fe0129a90c48b4d2ca31380724c09a8226/dtale-1.19.2-py2.py3-none-any.whl (7.7MB)\n",
            "\u001b[K     |████████████████████████████████| 7.7MB 3.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn>='0.21.0 in /usr/local/lib/python3.6/dist-packages (from dtale) (0.22.2.post1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from dtale) (1.15.0)\n",
            "Requirement already satisfied: future>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from dtale) (0.16.0)\n",
            "Collecting squarify\n",
            "  Downloading https://files.pythonhosted.org/packages/0b/2b/2e77c35326efec19819cd1d729540d4d235e6c2a3f37658288a363a67da5/squarify-0.4.3-py3-none-any.whl\n",
            "Requirement already satisfied: xarray in /usr/local/lib/python3.6/dist-packages (from dtale) (0.15.1)\n",
            "Collecting lz4; python_version > \"3.0\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/15/6a/ea95dd9a9957143636cfad5037637abec91016b9bde519d3edf4708e3d83/lz4-3.1.0-cp36-cp36m-manylinux2010_x86_64.whl (1.8MB)\n",
            "\u001b[K     |████████████████████████████████| 1.8MB 36.3MB/s \n",
            "\u001b[?25hCollecting dash>=1.5.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/69/91/ae029886dda55b93b60ac04377bcb2ab9209dd73244e3b5e513124cc6778/dash-1.17.0.tar.gz (75kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 7.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from dtale) (2.23.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from dtale) (1.4.1)\n",
            "Collecting dash-daq\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/16/c1/9c6adfc3646990965a5b2ca073f579cc9c09d15553fcfcd83aefd1255494/dash_daq-0.5.0.tar.gz (642kB)\n",
            "\u001b[K     |████████████████████████████████| 645kB 39.2MB/s \n",
            "\u001b[?25hCollecting dash-bootstrap-components; python_version > \"3.0\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/97/23/fc5bcf440d26725b809daf61e5e919a9e63f0aa993d0a5c0d54920705b75/dash_bootstrap_components-0.10.7-py2.py3-none-any.whl (185kB)\n",
            "\u001b[K     |████████████████████████████████| 194kB 38.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: itsdangerous in /usr/local/lib/python3.6/dist-packages (from dtale) (1.1.0)\n",
            "Collecting Flask-Compress\n",
            "  Downloading https://files.pythonhosted.org/packages/de/eb/6bb0f8cb872167752eab8b06b67724566342de873dcfd85faaf7761944d9/Flask-Compress-1.7.0.tar.gz\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from dtale) (1.1.3)\n",
            "Requirement already satisfied: Flask>=1.0 in /usr/local/lib/python3.6/dist-packages (from dtale) (1.1.2)\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.6/dist-packages (from dtale) (0.10.2)\n",
            "Collecting strsimpy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1e/c7/a957569f1ccfdff4e1886d571369318f1656e1372e287e880be1f495de04/strsimpy-0.1.9-py3-none-any.whl (45kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 4.6MB/s \n",
            "\u001b[?25hCollecting dash-colorscales\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d5/24/de9318acd52989d77135a8497c8be536442b725473684ed4e1ab999b2051/dash_colorscales-0.0.4.tar.gz (62kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 6.5MB/s \n",
            "\u001b[?25hCollecting flask-ngrok; python_version > \"3.0\"\n",
            "  Downloading https://files.pythonhosted.org/packages/af/6c/f54cb686ad1129e27d125d182f90f52b32f284e6c8df58c1bae54fa1adbc/flask_ngrok-0.0.25-py3-none-any.whl\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>='0.21.0->dtale) (0.17.0)\n",
            "Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>='0.21.0->dtale) (1.18.5)\n",
            "Requirement already satisfied: setuptools>=41.2 in /usr/local/lib/python3.6/dist-packages (from xarray->dtale) (50.3.2)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from dash>=1.5.0->dtale) (4.12.0)\n",
            "Collecting dash_renderer==1.8.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/72/fe/59a322edb128ad15205002c7b81e3f5e580f6791c4a100183289e05dbfcb/dash_renderer-1.8.3.tar.gz (1.0MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0MB 27.9MB/s \n",
            "\u001b[?25hCollecting dash-core-components==1.13.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/48/3dd8c7bf93cff3a9dd9d360bc25c4ec0cb191acdce13375c3ff85f98e992/dash_core_components-1.13.0.tar.gz (3.5MB)\n",
            "\u001b[K     |████████████████████████████████| 3.5MB 34.1MB/s \n",
            "\u001b[?25hCollecting dash-html-components==1.1.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/02/ba/bb9427c62feb25bfbaf243894eeeb4e7c67a92b426ed0575a167100e436e/dash_html_components-1.1.1.tar.gz (188kB)\n",
            "\u001b[K     |████████████████████████████████| 194kB 44.4MB/s \n",
            "\u001b[?25hCollecting dash-table==4.11.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bb/46/cc839f897cabea3f584cf59ce9d86a5ea3828e54eefe2537ba8b640a9707/dash_table-4.11.0.tar.gz (1.8MB)\n",
            "\u001b[K     |████████████████████████████████| 1.8MB 43.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->dtale) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->dtale) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->dtale) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->dtale) (2020.6.20)\n",
            "Collecting brotli\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b4/d3/7c98f05b7b9103e2f3a112ba42f269c798155b3e5404fb80bb8f823aaebe/Brotli-1.0.9-cp36-cp36m-manylinux1_x86_64.whl (357kB)\n",
            "\u001b[K     |████████████████████████████████| 358kB 45.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->dtale) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->dtale) (2.8.1)\n",
            "Requirement already satisfied: Werkzeug>=0.15 in /usr/local/lib/python3.6/dist-packages (from Flask>=1.0->dtale) (1.0.1)\n",
            "Requirement already satisfied: Jinja2>=2.10.1 in /usr/local/lib/python3.6/dist-packages (from Flask>=1.0->dtale) (2.11.2)\n",
            "Requirement already satisfied: click>=5.1 in /usr/local/lib/python3.6/dist-packages (from Flask>=1.0->dtale) (7.1.2)\n",
            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from statsmodels->dtale) (0.5.1)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->dash>=1.5.0->dtale) (1.3.3)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.10.1->Flask>=1.0->dtale) (1.1.1)\n",
            "Building wheels for collected packages: dash, dash-daq, Flask-Compress, dash-colorscales, dash-renderer, dash-core-components, dash-html-components, dash-table\n",
            "  Building wheel for dash (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash: filename=dash-1.17.0-cp36-none-any.whl size=83728 sha256=d231c4d1c41a8c965e74a71bf29ac39638b8ce5cf5455731e9ae0ea285250398\n",
            "  Stored in directory: /root/.cache/pip/wheels/e0/32/13/8a569b638a79cf80a4a721f56040d91198adec28e3bc8123fc\n",
            "  Building wheel for dash-daq (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-daq: filename=dash_daq-0.5.0-cp36-none-any.whl size=669704 sha256=5469a6149d1f0f4d0c56ebfda01642965c82156b42751f0ec07a899742e24dbd\n",
            "  Stored in directory: /root/.cache/pip/wheels/1c/9c/f7/84731716eaa9fc952cdbdd1f87df3ca9b8805c317c609efcd6\n",
            "  Building wheel for Flask-Compress (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for Flask-Compress: filename=Flask_Compress-1.7.0-cp36-none-any.whl size=6833 sha256=e8c46654162c6dbd9ac61bda84d479f35c156d43000b01673466ed790a820da7\n",
            "  Stored in directory: /root/.cache/pip/wheels/1d/b7/18/2b88ed33c5ef53868d1bfb0d3f2f351ae0143414734415bd06\n",
            "  Building wheel for dash-colorscales (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-colorscales: filename=dash_colorscales-0.0.4-cp36-none-any.whl size=62599 sha256=b5d72f1211aa9f32e7c9748fa19fd555f95553a4e7180bc401f55f7f0ef5cd5c\n",
            "  Stored in directory: /root/.cache/pip/wheels/0e/4a/1c/3490cfe6a59299b06917af6626a969c8fb7caead39b2d74cf2\n",
            "  Building wheel for dash-renderer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-renderer: filename=dash_renderer-1.8.3-cp36-none-any.whl size=1013944 sha256=5b9c9fd9b90ff070d5b52013bbd2f94800325c86564e6099a3353c3d7306494d\n",
            "  Stored in directory: /root/.cache/pip/wheels/cf/2b/5f/0928a6d1b7ebf280f21a2e925f36d662e6ba83e00b82c6b6bf\n",
            "  Building wheel for dash-core-components (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-core-components: filename=dash_core_components-1.13.0-cp36-none-any.whl size=3513142 sha256=f78649b6840caec39fc10cf2708b77b8f483fb5825f0470376abba8f250e2c92\n",
            "  Stored in directory: /root/.cache/pip/wheels/44/d9/90/177214625a5b5f6440b24ea807550e07fbf439a495eee4dd36\n",
            "  Building wheel for dash-html-components (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-html-components: filename=dash_html_components-1.1.1-cp36-none-any.whl size=427894 sha256=07e756359b78487f862101d4dacff9d28a693c6346f80f786fd85c03496d3ef8\n",
            "  Stored in directory: /root/.cache/pip/wheels/8d/01/46/78e4de185a8a4a2da8ba31da16c52170f036d4cebeeb6e07a2\n",
            "  Building wheel for dash-table (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-table: filename=dash_table-4.11.0-cp36-none-any.whl size=1839767 sha256=6ed41104ed49daaaa2659b9fa080c5f3d2ec9c6683bc332d50989d0bb43763ba\n",
            "  Stored in directory: /root/.cache/pip/wheels/ca/37/90/bd45dcc5d6acbe6ac53f753a8f1732ecf5270017372e389cd8\n",
            "Successfully built dash dash-daq Flask-Compress dash-colorscales dash-renderer dash-core-components dash-html-components dash-table\n",
            "Installing collected packages: squarify, lz4, brotli, Flask-Compress, dash-renderer, dash-core-components, dash-html-components, dash-table, dash, dash-daq, dash-bootstrap-components, strsimpy, dash-colorscales, flask-ngrok, dtale\n",
            "Successfully installed Flask-Compress-1.7.0 brotli-1.0.9 dash-1.17.0 dash-bootstrap-components-0.10.7 dash-colorscales-0.0.4 dash-core-components-1.13.0 dash-daq-0.5.0 dash-html-components-1.1.1 dash-renderer-1.8.3 dash-table-4.11.0 dtale-1.19.2 flask-ngrok-0.0.25 lz4-3.1.0 squarify-0.4.3 strsimpy-0.1.9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "https://g2n9w2jcpxm-496ff2e9c6d22116-40000-colab.googleusercontent.com/dtale/main/1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2dwvTF7GebQ",
        "outputId": "7d1bfcaf-de05-4de6-dc98-31ae3c306e7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        }
      },
      "source": [
        "print(model.ModelStats())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimization model '':\n",
            "#Variables: 2040 (17 in objective)\n",
            " - 1734 in [0,1]\n",
            " - 5 in [0,15]\n",
            " - 5 in [0,36]\n",
            " - 1 in [0,66]\n",
            " - 5 in [0,75]\n",
            " - 5 in [0,93]\n",
            " - 5 in [0,108]\n",
            " - 5 in [0,133]\n",
            " - 1 in [0,162]\n",
            " - 2 in [0,168]\n",
            " - 3 in [0,189]\n",
            " - 1 in [0,213]\n",
            " - 2 in [0,216]\n",
            " - 5 in [0,233]\n",
            " - 3 in [0,243]\n",
            " - 1 in [0,300]\n",
            " - 2 in [0,320]\n",
            " - 2 in [0,400]\n",
            " - 8 in [0,450]\n",
            " - 3 in [0,451]\n",
            " - 1 in [0,462]\n",
            " - 1 in [0,497]\n",
            " - 2 in [0,693]\n",
            " - 2 in [0,746]\n",
            " - 1 in [0,940]\n",
            " - 3 in [0,978]\n",
            " - 1 in [0,995]\n",
            " - 3 in [0,1054]\n",
            " - 6 in [0,1066]\n",
            " - 2 in [0,1493]\n",
            " - 5 in [0,2000]\n",
            " - 3 in [0,2108]\n",
            " - 1 in [0,2160]\n",
            " - 1 in [0,4500]\n",
            " - 1 in [0,5600]\n",
            " - 1 in [0,8000]\n",
            " - 1 in [0,8640]\n",
            " - 1 in [0,14000]\n",
            " - 1 in [0,15120]\n",
            " - 1 in [0,36000]\n",
            " - 204 in [0,1442570]\n",
            "#kInterval: 102\n",
            "#kLinear1: 204 (#enforced: 204)\n",
            "#kLinear2: 4896 (#enforced: 4896)\n",
            "#kLinearN: 17\n",
            "#kNoOverlap: 6\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}